Improved parameter estimation of the line-based transformation model for remote sensing image registration

Ahmed Shaker, Said M. Easa, Wai Yeung Yan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The line-based transformation model (LBTM), built upon the use of affine transformation, was previously proposed for image registration and image rectification. The original LBTM first utilizes the control line features to estimate six rotation and scale parameters and subsequently uses the control point(s) to retrieve the remaining two translation parameters. Such a mechanism may accumulate the error of the six rotation and scale parameters toward the two translation parameters. In this study, we propose the incorporation of a direct method to estimate all eight transformation parameters of LBTM simultaneously using least-squares adjustment. The improved LBTM method was compared with the original LBTM through using one synthetic dataset and three experimental datasets for satellite image 2D registration and 3D rectification. The experimental results demonstrated that the improved LBTM converges to a steady solution with two to three ground control points (GCPs) and five ground control lines (GCLs), whereas the original LBTM requires at least 10 GCLs to yield a stable solution.

Original languageEnglish
Article number32
JournalJournal of Imaging
Volume3
Issue number3
DOIs
Publication statusPublished - Sep 2017
Externally publishedYes

Keywords

  • Ground control lines
  • Image rectification
  • Image registration
  • Line-based transformation model
  • Remote sensing

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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